Thanks to breakthroughs in AI and Deep learning methodology, Computer vision techniques are rapidly improving. Most computer vision applications require sophisticated image segmentation to comprehend what is image and to make an analysis of each section easier. Training deep learning networks for semantic segmentation required a large amount of annotated data, which presents a major challenge in practice as it is expensive and labor-intensive to produce such data. The paper presents 1. Self-supervised techniques to boost semantic segmentation performance using multi-task learning with Depth prediction and Surface Normalization . 2. Performance evaluation of the different types of weighing techniques (UW, Nash-MTL) used for Multi-task learning. NY2D dataset was used for performance evaluation. According to our evaluation, the Nash-MTL method outperforms single task learning(Semantic Segmentation).

翻译：得益于人工智能和深度学习方法的突破，计算机视觉技术正迅速发展。大多数计算机视觉应用都需要复杂的图像分割来理解图像内容，并简化对每个区域的分析。训练用于语义分割的深度学习网络需要大量标注数据，这在实践中构成了重大挑战，因为生成此类数据既昂贵又耗时。本文提出：1. 利用深度预测和表面归一化的多任务学习，通过自监督技术提升语义分割性能；2. 对多任务学习中使用的不同加权技术（UW、Nash-MTL）进行性能评估。采用NY2D数据集进行性能评估。根据我们的评估，Nash-MTL方法优于单任务学习（语义分割）。